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All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal Jurnal Teliska TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Sinergi Annual Research Seminar EMITTER International Journal of Engineering Technology Proceeding of the Electrical Engineering Computer Science and Informatics VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Techno Bahari Jurnal Rekayasa Elektro Sriwijaya International Journal of Research in Vocational Studies (IJRVOCAS) Aptekmas : Jurnal Pengabdian Kepada Masyarakat International Journal of Advanced Science Computing and Engineering SmartComp International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) Jurnal Elektronika dan Telekomunikasi
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Object Detection Approach Using YOLOv5 For Plant Species Identification Billi Clinton; Amperawan Amperawan; Tresna Dewi
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.643

Abstract

In the modern era of agriculture and horticulture, biodiversity conservation requires plant species identification skills, and automatic detection is a challenging and interesting task. However, many factors often make some people mistaken in recognizing plant species that have unique and varied visual characteristics, making manual identification difficult. This problem requires an effective and accurate model for identifying plant species. So this research aims to produce a model to identify plant species that are effective and have a high level of accuracy. This research offers the use of the YOLOv5 algorithm method. The training process with epoch 200 and 53 minutes with a total of 1,220 images. Based on the results of the model performance test, the mAP value was 85.73%, precision 98.27%, and recall 94.36%. During testing, the model can identify plant species accurately on single objects and multiple objects. The results of this research show that the proposed method is successful in identifying plant species accurately.
Multistage Fertile Egg Prediction via Texture Using Convolutional Neural Network Bimo, Muhammad; Dewi, Tresna; Maulidda, Renny; Oktarina, Yurni; Risma, Pola; Yudha, Hendra Marta
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/q58ezz91

Abstract

Accurate early detection of egg fertilisation status is necessary for effective incubation management in chicken production in order to avoid energy waste and decreased hatchery productivity brought on by infertile or non-viable eggs. Due to their comparable perceptual traits, conventional candling inspection relied on manual observation, which introduced subjectivity and made it challenging to distinguish between fertilised and blighted eggs early on. This study suggested an automated multistage fertilisation prediction method based on candling image analysis, utilising a convolutional neural network framework to get around this restriction. Rather than using traditional binary classification, the suggested system allowed for progressive monitoring of embryonic growth. On incubation days 1, 7, 14, and 21, candling photos were taken from native chicken eggs and classified into three groups: fertilised, infertile, and blighted. To enhance feature extraction efficiency under constrained dataset conditions, a transfer learning technique utilising the MobileNetV2 architecture was implemented. To guarantee consistent learning performance, image preprocessing, augmentation, model training, and validation were carried out. Precision, recall, F1-score, and classification accuracy were used as assessment measures. According to experimental findings, the suggested model produced consistent classification results for both fertilised and infertile eggs, with validation accuracy ranging from 90 to 95% throughout the incubation period. The results of multistage prediction showed consistent decision-making throughout the observation of embryo development. However, during intermediate incubation stages, visual uncertainty with fertilised eggs led to decreased performance in recognising blighted eggs. All things considered, the suggested method showed great promise as a nondestructive intelligent system for early fertilisation prediction. To increase the accuracy of blighted egg classification, more dataset expansion and model improvement were still required.
Co-Authors A Rahman Ahmad Fudholi Alkausar, Muhammad Fajri Amalia, Kania Yusriani Amperawan Amperawan Amperawan Amperawan, Amperawan Angga Prasetia Anggraini, Citra Arissetyadhi, Iwan Auliya, Annisa Azhar, M. Sayid Badruzzaman, Farid Bambang Tutuko Bambang, Muhammad Refo Billi Clinton Bimo, Muhammad Clinton, Billi Dadi Setiadi Daniesar, Muhammad Nouval Dicky Astra Yudha Didi Suhaedi Dinata, Yogi Edo Triyandi Erwin Harahap Evelina Ginting Fajar, Yusuf Fatahul Arifin, Fatahul Fradina Septiarini Hendra Marta Yudha Hibrizi, Dzaky Rafif Husni, Nyayu Latifah INDRAYANI INDRAYANI Indriyani Indriyani Junaedi, Ketut Juwita, Aulia Ratna Kemala Dewi Kusumanto, Raden Liwijaya, Angga Lukman Nul Hakim M. Muhajir Mardianto, Yudhi Mardiyati, Elsa Nurul Maulidina, Elfira Mayastri Devana Muhammad Dede Yusuf Muhammad Insan Kamil, Muhammad Insan Muhammad Nawawi Muhammad Ridho Kenawas Muhammad Roriz Muhammad Taufik Roseno Mulya, Zarqa Muslikhin Mustofa Mustofa Neta Larasati Noer, Mohammad Nawawi Nur Mutiara Syahrian Oktarina, Yurni Oktarina, Yurni Pola Risma Putri Repina Kesuma Rapli Wijaya RD Kusumanto RD Kusumanto Rendi Dwi Yanto Renny Maulidda, Renny Rinaldi Rinaldi Riyo Irawan Robiansyah Ronald Sukwadi Roseno, M. Taufik Rusdianasari Rusdianasari Rusdianasari Rusdianasari Rusdianasari Sakuraba, Takahiro Sasmanto, Andri Agus Sastiani, Destri Zumar SELAMET MUSLIMIN Septiyani AR, Dini Siproni Siproni Siproni Umar Siti Afiyah Qatrunnada Siti Nurmaini Solly Aryza Sri Rezki Artini Syahrian, Nur Mutiara Tampubolon, Debora Utami, Retyo Wizi Nafa Velia Yuliza Wahju, Marsellinus Bachtiar Wijanarko, Yudi Wijaya Pratama, Agung Yohandri Bow Yudha Wira Pratama Yudi Wijanarko Yudi Wijanarko, Yudi Yurika Islamiati Yurni Oktarina Yurni Oktarina Yurni Oktarina Yusi, Muhammad Syahirman Zarqa Mulya